Abstract
Taking an extension of resolution as a base calculus (though its principles are applicable to other calculi) for searching proofs (refutations) and counterexamples (models), we introduce a new method able to find refutations and also models by analogy with refutations and models in a knowledge base. The source objects for the analogy process are generalizations of the refutations (models). They are included in the knowledge base, and then unification techniques for the choice of the relevant source objects as well as the building of a new proof or a model by analogy are used. These steps are rather standard, and can hardly be avoided. Our method follows these steps but incorporates original contributions on each of them. A method to build new proofs as well as models by analogy with existing ones is proposed. Some comparisons with existing methods as well as two detailed running examples on generalization show evidence of the interest of our approach.
This work has been funded by MRE-France
This work has been funded by DGA-France
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Bourely, C., Défourneaux, G., Peltier, N. (1996). Building proofs or counterexamples by analogy in a resolution framework. In: Alferes, J.J., Pereira, L.M., Orlowska, E. (eds) Logics in Artificial Intelligence. JELIA 1996. Lecture Notes in Computer Science, vol 1126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61630-6_3
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DOI: https://doi.org/10.1007/3-540-61630-6_3
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